import numpy as np
from 深度学习入门基于python的理论与实践.common.functions import sigmoid, cross_entropy_error, softmax, sigmoid_grad
from 深度学习入门基于python的理论与实践.common.gradient import numerical_gradient
class TwoLayerNet:
    def __init__(self, input_size, hidden_size, output_size, weight_init_std=0.01):
        self.params = {}
        self.params["W1"] = weight_init_std * np.random.randn(input_size, hidden_size)
        self.params["b1"] = np.zeros(hidden_size)
        self.params["W2"] = weight_init_std * np.random.randn(hidden_size, output_size)
        self.params["b2"] = np.zeros(output_size)

    def predict(self, x):
        W1, W2 = self.params["W1"], self.params["W2"]
        b1, b2 = self.params["b1"], self.params["b2"]
        a1 = np.dot(x, W1) + b1
        z1 =sigmoid(a1)
        a2 = np.dot(z1, W2) + b2
        y = sigmoid(a2)
        return y

    def loss(self, x, t):
        y = self.predict(x)
        return cross_entropy_error(y, t)

    #模型输出准确率
    def accuracy(self, x, t):
        y = self.predict(x)
        y = np.argmax(y, axis=1)
        t = np.argmax(t, axis=1)
        accuracy = np.sum(y == t) / float(x.shape[0])
        return accuracy

    # 梯度下降，计算神经网络各层权重和偏置的梯度
    def numerical_gradient(self, x, t):
        # 定义一个损失函数
        loss_W = lambda W: self.loss(x, t)
        grads = {}
        grads["W1"] = numerical_gradient(loss_W, self.params["W1"])
        grads["b1"] = numerical_gradient(loss_W, self.params["b1"])
        grads["W2"] = numerical_gradient(loss_W, self.params["W2"])
        grads["b2"] = numerical_gradient(loss_W, self.params["b2"])
        return grads

    def gradient(self, x, t):
        W1, W2 = self.params['W1'], self.params['W2']
        b1, b2 = self.params['b1'], self.params['b2']
        grads = {}

        batch_num = x.shape[0]

        # forward
        a1 = np.dot(x, W1) + b1
        z1 = sigmoid(a1)
        a2 = np.dot(z1, W2) + b2
        y = softmax(a2)

        # backward
        dy = (y - t) / batch_num
        grads['W2'] = np.dot(z1.T, dy)
        grads['b2'] = np.sum(dy, axis=0)

        da1 = np.dot(dy, W2.T)
        dz1 = sigmoid_grad(a1) * da1
        grads['W1'] = np.dot(x.T, dz1)
        grads['b1'] = np.sum(dz1, axis=0)

        return grads

if __name__ == "__main__":
    net = TwoLayerNet(input_size=784, hidden_size=100, output_size=10)
    print(net.params["W1"].shape)
    print(net.params["b1"].shape)
    print(net.params["W2"].shape)
    print(net.params["b2"].shape)

    # x = np.random.randn(100, 784)
    # y = net.predict(x)
    # print(y)

    x = np.random.randn(100, 784)
    t = np.random.randn(100, 10)
    grads = net.numerical_gradient(x, t)
    print(grads["W1"].shape)
    print(grads["b1"].shape)
    print(grads["W2"].shape)
    print(grads["b2"].shape)

